Systems, apparatus and methods for controlling a movement of a cell culture to optimize cell growth

ABSTRACT

A system for controlling a motion of a cell culture includes a tray adapted to hold a cell culture, a camera adapted to capture an image of the cell culture, and a device adapted to control a movement of the tray. The system also includes a processor adapted to determine a first movement of the tray, receive from the camera data representing an image of the cell culture, determine a characteristic of the cell culture based on the image data, determine a second movement of the tray based on the characteristic, the second movement being different from the first movement, and cause the tray to move in accordance with the second movement.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/703,500, filed Sep. 13, 2017, which in turn claims the benefit ofU.S. Provisional Application Ser. No. 62/394,569 filed Sep. 14, 2016,the disclosure of which is incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present invention relates generally to culturing cells, and moreparticularly, to systems, apparatus, and methods for controlling amovement of a cell culture to optimize cell growth.

BACKGROUND

The process of culturing cells requires providing nutritive componentsto an initial population of cells, whether from a pre-existing orrecently isolated cell line, followed by incubation in a sterilevessel/container to facilitate cell proliferation. Existing cell culturemethods include, for example, the cover glass method, the flask method,the rotating tube method and the like. Generally, a cell culturesolution/media is used to promote the growth of the initial cellpopulation by providing needed vitamins, amino acids and other nutrientsto facilitate cell growth.

The culture of living cells makes it possible to obtain a cellpopulation from a single cell, and may be performed for various purposessuch as, for example, the recovery of additional by-products generatedby cellular metabolism, the preparation of viral vaccines, cellgeneration to fabricate an artificial organ or to re-populate ade-cellularized organ scaffold, the production of pharmaceuticals byrecombinant expression within eukaryotic (e.g., animal) cell lines, etc.

Typically, the process of cell culture requires a suitable container forculturing cells, a culture solution/media for supplying nutrition to thecells, and various gases, such as oxygen, to facilitate cell growth. Theculture solution/media and various gases are introduced (e.g., injected)into the culture space of the container and used to culture cells.Examples of such culture solution/media include fetal bovine serum(“FBS”) and bovine calf serum (“BCS”), although new regulatory trendslean toward minimizing or avoiding the use of FBS/BCS as a culturesolution/medium. Periodically, the culture solution/media and thevarious gasses are replaced to maintain the cells in a fresh conditionand to stimulate cell growth. In the alternative, culture solution/mediaand the various gasses are replaced on a continuous basis to maintainthe cells in a fresh condition and to stimulate cell growth. By thecontinuous replacement of solution/media and the fine control of variousgases, constant optimal levels of cell nutrients are obtained, andtherefore FBS/BCS quantities are minimized, or new culture media that donot contain FBS/BCS can be adopted.

In addition, it is also desirable to ensure that cells growing in theculture space of the container are uniformly distributed to facilitatethe supply of the culture solution/media and gases to the cells.However, in existing cell culture devices, the cells in the culturespace often fail to grow in a uniformly distributed manner. For example,in many existing cell culture devices, cells grow in irregularlydistributed patterns due to natural patterns of cell growth, the flow ofthe culture solution through the culture space of the container, or forother reasons not immediately known.

SUMMARY

In accordance with an embodiment, a method is provided. An image of acell culture is generated, and a characteristic of the cell culture isdetermined based on the image. A movement of the cell culture isadjusted based on the characteristic to facilitate cell growth.

In one embodiment, motion data indicating a motion of a tray is receivedfrom a sensor. A first movement of the tray is determined based on themotion data. The movement of the cell culture is adjusted by determininga second movement of the tray based on the characteristic, the secondmovement being different from the first movement.

In another embodiment, the characteristic comprises a measure of celldensity.

In another embodiment, a camera is used to capture an image of the cellculture, and the image data is analyzed to determine the measure of celldensity.

In another embodiment, determining the measure of cell density includesdetermining a count of cell clusters. A determination is made whetherthe measure of cell density exceeds a predetermined limit, and themovement of the cell culture is adjusted in response to determining thatthe measure of cell density exceeds the predetermined limit. In anotherembodiment, determining the measure of cell density includes determiningone or more counts of cells representing cells with differentmorphologies. One or more measures of cell densities may be determinedbased on the one or more counts of cell morphologies.

In another embodiment, the cell culture is disposed in a tray with cellsdisposed either in adherence to the tray or in suspension in the culturesolution. A tilting motion of the tray and/or a shaking motion of thetray is adjusted. Adjusting a tilting motion of the tray may includecausing the tray to tilt back and forth at a lower or higher rate.Adjusting a shaking motion of the tray may include causing the tray toshake back and forth at a lower or higher rate.

In accordance with another embodiment, an apparatus includes a firstdevice adapted to hold a cell culture container and to cause a movementof the cell culture in the container. The apparatus also includes asecond device adapted to generate an image of the cell culture in thecontainer, and at least one processor adapted to determine acharacteristic of the cell culture based on the image, and to cause thefirst device to adjust the movement of the cell culture container, basedon the characteristic.

In one embodiment, the characteristic comprises a measure of celldensity.

In one embodiment, the characteristic comprises at least one measure ofcell density determined based on a determination of different cellmorphologies.

In another embodiment, the processor is further adapted to determine ameasure of average cell density based on the image.

In accordance with another embodiment, a system includes a tray adaptedto hold a cell culture container, a camera adapted to capture an imageof the cell culture within the container, and a device adapted tocontrol a movement of the tray. The system also includes a processoradapted to determine a first movement of the tray, receive from thecamera data representing an image of the cell culture, determine acharacteristic of the cell culture based on the image data, determine asecond movement of the tray based on the characteristic, the secondmovement being different from the first movement, and cause the deviceto cause the tray to move in accordance with the second movement.

In one embodiment, the system also includes a sensor adapted to obtainmotion data indicating a motion of the tray. The processor is furtheradapted to receive the motion data from the sensor and determine thefirst movement of the tray based on the motion data.

In another embodiment, the characteristic comprises a measure of celldensity.

In another embodiment, the processor is further adapted to determine ameasure of cell density based on the image data.

In another embodiment, the processor is further adapted to determine acount of cell clusters, and determine the measure of cell density basedon the count of cell clusters.

In another embodiment, the processor is further adapted to determinethat the measure of cell density exceeds a predetermined limit, anddetermine the second movement in response to the determination that thatthe measure of cell density exceeds the predetermined limit.

In another embodiment, the processor is further adapted to determine oneor more different morphologies of cells in a culture, determine one ormore counts of cells having different characteristics such as shape,size, etc., and determine one or more measures of cell densitiesaccording to the different cell types.

In another embodiment, the processor is further adapted to adjust one ofa tilting motion of the tray and a shaking motion of the tray todetermine the second movement of the tray. The processor may be adaptedto cause the tray to tilt back and forth at a lower or higher rate or tocause the tray to shake back and forth at a lower or higher rate.

These and other advantages of the present disclosure will be apparent tothose of ordinary skill in the art by reference to the followingDetailed Description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows components of a cell culture system in accordance with anembodiment;

FIG. 2 shows components of a cell culture system in accordance withanother embodiment;

FIG. 3A shows a perspective view of a tray control system in accordancewith an embodiment;

FIG. 3B shows a perspective view of a tray control system in accordancewith another embodiment;

FIG. 4 shows a top view of the frame, plate, and tray of the embodimentof FIG. 3;

FIG. 5 shows a top view of a frame, plate, and tray in accordance withanother embodiment;

FIGS. 6A-6C show the operation of a tilt mechanism in accordance with anembodiment;

FIG. 6D shows components of a tilt controller in accordance with anembodiment;

FIG. 7 shows a top view of the plate of the embodiment of FIG. 3;

FIGS. 8A-8C show the operation of a shake mechanism in accordance withan embodiment;

FIG. 8D shows components of a shake controller in accordance with anembodiment;

FIG. 9 shows a perspective view of the underside of the frame, plate,and shake mechanism of the embodiment of FIGS. 8A-8C;

FIG. 10 shows a cell culture disposed on a tray in accordance with anembodiment;

FIG. 11A is a flowchart of a method of controlling a movement of a trayin accordance with an embodiment;

FIG. 11B is a flowchart of a method of controlling a movement of a cellculture in accordance with another embodiment;

FIG. 12A shows a side view of a frame and tilt mechanism supporting abag with cell culture in accordance with an embodiment;

FIG. 12B shows the side view of a frame and tilt mechanism supportingmultiple bags with cell culture in accordance with an embodiment;

FIG. 12C shows a side view of the frame and tilt mechanism supporting abag with cell culture in which the frame is equipped with tilting servosin accordance with an embodiment;

FIG. 13 shows components of an exemplary computer that may be used toimplement certain embodiments;

FIG. 14 shows a camera supported by an arm in accordance with anembodiment; and

FIGS. 15A-15B show components of a bioreactor system in accordance withan embodiment.

DETAILED DESCRIPTION

In accordance with an embodiment, a cell culture system includes a firstdevice adapted to hold a cell culture and to cause the cell culture tomove in a manner selected to optimize cell growth. The apparatus alsoincludes a second device adapted to generate an image of the cellculture, and at least one processor adapted to determine acharacteristic of the cell culture based on the image, and to cause thefirst device to adjust the movement of the cell culture, based on thecharacteristic.

FIG. 1 shows components of a cell culture system in accordance with anembodiment. Cell culture system 100 includes a tray control system 110and a computer 120. Tray control system 110 is adapted to hold a cellculture and to move the cell culture in a manner that facilitates andoptimizes cell growth. Computer 120 may receive data from tray controlsystem 110 and may transmit control signals to tray control system 110.Computer 120 may be any suitable processing device such as a servercomputer, a personal computer, a laptop device, a cell phone, etc.

FIG. 2 shows components of cell culture system 100 in accordance withanother embodiment. Cell culture system 100 includes a tray 220, a tiltmechanism 230, a shake mechanism 240, a power source 250, a camera 260,an accelerometer 270, an image analyzer 280, and a tray motioncontroller 290. Cell culture system 100 may include components not shownin FIG. 2.

Tray 220 includes a surface adapted to hold a cell culture. Tray 220 mayhave any shape; tray 220 may be square, rectangular, circular, oranother shape. Tray 220 may include more than one surface. In oneembodiment, a cell culture is contained in a container disposed directlyon the surface of tray 220. In another embodiment, a cell culture may becontained in a bag, or other enclosure that is disposed on tray 220.Tray 220 may be made from plastic (including transparent plastics),metal, or any other suitable material.

Tilt mechanism 230 causes tray 220 to tilt, i.e., to change itsorientation from a horizontal position (wherein tray 220 is disposed ina horizontal plane) to a selected non-horizontal position (wherein trayis disposed in a second non-horizontal plane). For example, tiltmechanism 230 may cause tray 220 to move back and forth between a firstnon-horizontal plane (defined by a first predetermined angle relative tothe horizontal plane) and a second non-horizontal plane (defined by asecond predetermined angle relative to the horizontal plane) at aselected speed or acceleration. Tilt mechanism 230 may operate inresponse to control signals received from a processing device.

Shake mechanism 240 causes tray 220 to shake, i.e., to move from side toside at a selected speed or acceleration. Shake mechanism 240 mayoperate in response to control signals received from a processingdevice.

Power source 250 provides power to cell culture system 100. For example,power source 250 may include one or more batteries. Cell culture system100 may include more than one power source.

Camera 260 obtains images of a cell culture disposed on tray 220. Camera260 may be a digital camera, for example. Camera 260 may provide digitalimage data to a computer or other processing device for analysis.

Accelerometer 270 is a sensor adapted to obtain data indicating theacceleration of tray 220. Accelerometer 270 may also measure otherparameters including the speed and/or other motions of tray 220, forexample. Accelerometer 270 may provide acceleration and/or other motiondata to a computer or other processing device.

In other embodiments, cell culture system 100 may include other types ofsensors such as sensors to measure temperature, mass, weight, pH, gaslevels (O₂ and CO₂), etc.

Image analyzer 280 analyzes image data generated by camera 260 anddetermines one or more characteristics of the cell culture disposed ontray 220. For example, image analyzer 280 may analyze an image of a cellculture and determine a measure of cell density. Image analyzer 280 maytransmit information (e.g., a measure of cell density or otherinformation) to tray motion controller 290.

Tray motion controller 290 controls the motion of tray 220. For example,tray motion controller 290 may cause tilt mechanism 230 to tilt tray220. Tray motion controller 290 may cause shake mechanism 240 to shaketray 220. From time to time, tray motion controller 290 may receiveinformation from image analyzer 280 and, based on the information, causetilt mechanism 230 or shake mechanism 240 to adjust the motion of tray220. For example, tray motion controller 290 may receive from imageanalyzer 280 a measure of cell density (which may include, for example,a measure of average cell density, one or more measures of celldensities according to different cell morphologies, etc.) and, based onthe measure of cell density, causing tilt mechanism 230 or shakemechanism 240 to adjust the motion of tray 220.

One or more components shown in FIG. 2 may be implemented by a computersuch as computer 120 of FIG. 1. For example, image analyzer 280 and/ortray motion controller 290 may comprise software (and/or circuitry)residing and operating on computer 120.

FIG. 3A shows a perspective view of tray control system 110 inaccordance with an embodiment. Tray control system 110 includes a frame350. Shake mechanism 240 includes a rectangular plate 305 which isdisposed within frame 350 and is coupled to frame 350 by four coils 320.Tray 220 is disposed on a top surface of shake mechanism 240. Shakemechanism 240 also includes a shake controller 860 (located underneathplate 305). Tilt mechanism 230 is connected to a side of frame 350.Camera 260 is positioned above tray 220 by an arm 364, which may beconnected to a side of frame 350, for example.

FIG. 3B shows a perspective view of tray control system 110 inaccordance with another embodiment. Tray control system 110 includesframe 350, shake mechanism 240, and rectangular plate 305. Tray 220 isdisposed on the top surface of shake mechanism 240. Tilt mechanism 230is connected to the side of frame 350. A rod 370 is positioned above oneside of frame 350. A sliding mechanism 392 is adapted to slide along rod370. Sliding mechanism 392 is connected to and supports an arm 394,which holds camera 260 above tray 220. Because arm 394 is connected tosliding mechanism 392, the camera 260 may be moved from one end of frame350 to the other end, to obtain various views of tray 220 (and variousviews of any culture located on tray 220). Tray control system 110 alsoincludes a controller 380 adapted to control the movement of slidingmechanism 392. Thus, controller 380 is adapted to cause camera 260 tomove from a first position to a second, selected position to obtain animage of a selected portion of tray 220.

FIG. 4 shows a top view of frame 350, plate 305, and tray 220 of theembodiment of FIG. 3. Plate 305 is separated from frame 350 by a gap ofwidth “W”. The gap between plate 305 and frame 350 allows plate 305 (andtray 220, which is disposed on plate 305) to move within frame 350. Inone embodiment, width “W” is between 5.0-20.0 millimeters.

FIG. 5 shows a top view of frame 350, plate 305, and tray 220 inaccordance with another embodiment. Accelerometer 270 is disposed ontray 220. In other embodiments, accelerometer 270 may be placed inanother location or position.

In accordance with an embodiment, a cell culture within a container isdisposed on tray 220, and the motion of the cell culture is controlledto optimize cell growth. The motion of the cell culture may becontrolled by controlling the motion of tray 220, for example.Specifically, tray 220 may tilt back and forth. Alternatively, tray 220may shake in a back-and-forth motion. Moving tray 220 in such a motioncauses the cell culture disposed on tray 220 to move in a similarmotion. Moving the cell culture should cause the distribution of cellsin the cell culture to change; for example, a rapid tilting or shakingmotion may cause cells that are in clusters to separate, therebydecreasing the cell density within the cell culture. Advantageously,decreasing the cell density may facilitate the growth of cells in thecell culture. However, movement of tray 220 should be regulated to avoidexcessive shear within the cell culture that can lead to disruption ofthe cell membrane and unwanted cell death.

FIGS. 6A-6C show the operation of tilt mechanism 230 in accordance withan embodiment. Referring to FIG. 6A, tilt mechanism 230 includes asupport arm 620 connected to frame 350. A tilt controller 630 isattached to support arm 620. A rotating piece 640, which has fourrotating arms, is attached to support arm 620 and is controlled by tiltcontroller 630. A lever 610 is connected at a first end to one of therotating arms of rotating piece 640 by a connector 667 and at a secondend to frame by a second connector 664. Connectors 664 and 667 may bescrews, for example, or another type of fastener.

Referring to FIG. 6B, tilt controller 630 from time to time causesrotating piece 640 to rotate in a counter-clockwise direction. Whenrotating piece 640 rotates in a counter-clockwise direction, rotatingpiece 640 pulls lever 610, which in turn causes an end 690 of frame 350to tilt downward. When frame 350 tilts to one side, plate 240 and tray220 also tilt in a similar manner.

Referring now to FIG. 6C, tilt controller 630 from time to time causesrotating piece 640 to rotate in a clockwise direction. When rotatingpiece 640 rotates in a clockwise direction, rotating piece 640 pusheslever 610, which in turn causes end 690 of frame 350 to tilt upward.When frame 350 tilts to one side, plate 240 and tray 220 also tilt in asimilar manner.

FIG. 6D shows components of tilt controller 630 in accordance with anembodiment. Tilt controller 630 includes a processor 682, a memory 684,and a transceiver 686. Processor 682 controls the movement of rotatingpiece 640. Processor 682 may from time to time store data in memory 684.Transceiver 686 may from time to time receive control signals (e.g.,from tray motion controller 290 or from other components). Transceiver686 may include an antenna, for example.

Referring again to FIG. 3, tray 220 rests on plate 305. FIG. 7 shows atop view of plate 305 in accordance with an embodiment. Plate 305 hashole 725 at or near the center of the plate. Hole 725 passes throughplate 305. Plate 305 may be made from plastic, metal, or anothersuitable material. Plastics may include transparent plastics, whichallow transmitted light mode imaging. Thus, in one embodiment, tray 220may comprise a transparent plastic, plate 305 may also comprise atransparent plastic; in such case, transparent light mode imaging may beused. Hole 725 may have a diameter between 1.0 centimeters and 5.0centimeters, for example. Other diameters may be used.

FIGS. 8A-8C show the operation of shake mechanism 240 in accordance withan embodiment. FIG. 8A shows a cross-sectional view of tray 220 andcomponents of shake mechanism 240. Tray 220 rests on plate 305. Tray 220includes a projecting member 810 which projects from the underside oftray 220 and fits through hole 725.

Shake mechanism 240 includes a rotating piece 820, shake controller 860,and one or more connectors 840. Rotating piece 820 includes a firstcavity 822 and a second cavity 826. Shake controller 860 has a spinningmember 865.

Referring to FIG. 8B, projecting member 810 of tray 220 fits into firstcavity 822 of rotating piece 820. Spinning member 865 of shakecontroller 860 fits into second cavity 826 of rotating piece 820.Connectors 840 connect shake controller 860 to plate 305. In otherembodiments, other types of connectors may be used to connect shakecontroller 860 to plate 305. For example, shake controller 860 may beheld in a basket which is connected to plate 305.

In accordance with an embodiment, shake controller 860 causes spinningmember 865 to spin. Spinning member 865 is fixed within cavity 826 ofrotating piece 820. Consequently, as spinning member 865 spins, itcauses rotating piece 820 to rotate around spinning member 865, therebycausing projecting member 810 of tray 220 to rotate in a circle withinhole 725. FIG. 8C shows tray 220 and the components of shake mechanism240 after rotating piece has rotated approximately one hundred eighty(180) degrees relative to the position shown in FIG. 8B. This motion hascaused projecting member 810, and tray 220, to move.

In one embodiment, shake controller 860 may cause spinning member 865 tospin at between 10 and 300 rotations per second. Other rates of rotationmay be used. The rotating motion of projecting member 810 causes tray220 to move in a circular motion on top of plate 305. The circularmotion of tray 220 imparts a shaking motion to any cell culture disposedon tray 220.

FIG. 8D shows components of shake controller 860 in accordance with anembodiment. Shake controller 860 includes a processor 882, a memory 884,and a transceiver 886. Processor 882 controls the movement of spinningmember 865. Processor 882 may from time to time store data in memory884. Transceiver 886 may from time to time receive control signals(e.g., from tray motion controller 290 or from other components).Transceiver 886 may include an antenna, for example.

FIG. 9 shows a perspective view of the underside of frame 350, plate305, and shake mechanism 240 of the embodiment of FIGS. 8A-8C. In otherembodiments, shake mechanism 240 may be configured differently and/ormay operate in a different manner. In accordance with an embodiment,cell culture system 100 may be used to optimize cell growth in a cellculture. Cell culture system 100 can be a batch reactor system, a fedbatch reactor system or a continuous reactor system. Such systems arewell known in the art. Cell culture system 100 can also be modularizedfor ease of use.

In an illustrative example, a container containing a cell culture isplaced on tray 220, and the tray is moved in accordance with apredetermined pattern. For example, the tray may be tilted back andforth at a first selected rate in order to facilitate a uniformdistribution of cells. One or more images of the cells are captured.Motion data indicating the motion of the tray is also obtained. Theimage data is analyzed to determine a measure of cell density within thecell culture. An adjusted motion of the tray is determined based on theimage data and the motion data. For example, supposing that the measureof cell density is determined to exceed a predetermined limit, anadjusted motion selected to decrease cell density may be determined. Forexample, the adjusted motion may include tilting the tray at a secondselected rate (faster than the first rate) and/or at a selected angle,and may further include shaking the tray at a third selected rate. Thetray is then caused to move in accordance with the adjusted motion.

For example, in an illustrative embodiment shown in FIG. 10, a cellculture 1000 is disposed on tray 220. Tray motion controller 290 nowuses tilt mechanism 230 and shake mechanism 240 to cause tray 220 tofollow a predetermined motion. For example, tilt mechanism 230 and shakemechanism 240 may be used to cause tray 220 to tilt back and forth at afirst predetermined rate, and to shake back and forth at a secondpredetermined rate.

Cell culture system 100 is now used to monitor cell growth in cellculture 1000 and control (and adjust) the motion of tray 220 in order tooptimize the cell growth. For example, cell growth may be facilitated bydetermining if an undesirably high level of cell density occurs in thecell culture and, in response, adjusting the motion of tray 220 tofacilitate cell growth within the cell culture in a manner that reducesthe cell density.

FIG. 11A is a flowchart of a method of controlling a motion of a cellculture in accordance with an embodiment. At step 1110, image datarepresenting an image of a cell culture on a tray is received. In theillustrative embodiment, camera 260 captures one or more images of cellculture 1000. Camera 260 converts the image into image data andtransmits the image data to image analyzer 280.

At step 1115, a measure of cell density is determined based on the imagedata. Image analyzer 280 receives the image data from camera 260 andanalyzes the image data to generate a measure of cell density. Any oneof a variety of methods may be used to generate a measure of celldensity. For example, image analyzer 280 may identify all cells in theimage and calculate a measure of average cell density. In anotherembodiment, image data may be used to identify different cellmorphologies (size, shape, etc.) among the cells in the cell culture anddetermine one or more measures of cell densities based on the differentcell morphologies. Alternatively, image analyzer 280 may examine cellsin the cell culture to identify features that meet predeterminedcriteria. For example, image analyzer 280 may identify regions where“cell clusters” are forming, wherein a “cluster” is defined as a regionhaving a cell density above a predetermined limit. Image analyzer 280may then use a count of the number of such regions as a measure of celldensity. Other measures may be used. The measure of cell density isprovided to tray motion controller 290.

At step 1120, motion data relating to a motion of the tray is received.In the illustrative embodiment, accelerometer 270 generates motion dataand transmits the motion data to tray motion controller 290. The motiondata may include, without limitation, data indicating acceleration,speed, direction of motion, etc. Tray motion controller 290 may receivemultiple measurements of motion over a selected period of time.

At step 1125, a current movement of the tray is determined based on themotion data. Tray motion controller 290 analyzes the motion datareceived from accelerometer 270 and determines a current movement oftray 220. For example, tray motion controller 290 may determine, basedon the motion data, that tray 220 is at rest, or that tray 220 is movingin a particular direction at a particular speed and acceleration, orthat tray 220 is following a pattern of motion such as a back-and-forthmotion, etc.

At step 1130, an adjusted movement of the tray is determined, based onthe image data and motion data. In the illustrative embodiment, traymotion controller 290 analyzes the cell density information and themotion data and determines whether an adjustment to the tray's movementis desirable in order to optimize or improve cell growth. Supposing thattray motion controller 290 determines that an adjusted movement isrequired, the adjustment to the tray's movement may include anadjustment to the tilting motion of tray 220 and/or an adjustment to thehorizontal (shaking) movement of tray 220. For example, tray motioncontroller 290 may determine that cell density exceeds a predeterminedlimit and, in response, determine that the tilting motion of tray 220should be adjusted by tilting the tray to a higher angle, and/or bytilting the tray back and forth at a higher rate, or may determine thatthe shaking motion of tray 220 should be adjusted by shaking the tray ata higher rate, etc.

At step 1140, the tray is caused to move in accordance with the adjustedmovement. Tray motion controller 290 causes tilt mechanism 230 and shakemechanism 240 to adjust the tray's motion in order to effect theadjusted movement determined at step 1130. Thus, tray motion controller290 may cause tilt mechanism 230 to tilt tray 220 at a faster or slowerrate, for example, and/or may cause shake mechanism 240 to shake tray220 at a faster or slower rate. For example, tray motion controller 290may generate and transmit control signals to tilt mechanism 230 and/orto shake mechanism 240 to effect the adjusted movement.

In other embodiments, other characteristics of a cell culture disposedon tray 220 may be determined and used to adjust the motion of the tray.For example, image analyzer 280 and/or one or more sensors may be usedto determine, without limitation, a measure of a color of a cellculture, a measure of a temperature of a cell culture, a measure of aweight of a cell culture, one or more measures of different celldensities according to different cell morphologies, a measure oftransparency or opaqueness of a cell culture, etc., may be determined.Alternatively, patterns of cell growth may be determined from the imagedata. Adjustments to the motion of tray 220 may be determined andapplied based on these observed and measured characteristics.

In another embodiment, a measure of cell density may be determined byexamining an image of a cell culture and defining one or more “cellareas” containing one or more cells. For example, two cells that arelocated within a predetermined distance of another cell may beconsidered to be within the same cell area. An outline is defined aroundthe perimeter of each cell area. The total area occupied by cell areasis determined. A measure of cell density may then be determined based onthe total area occupied by cell areas, with respect to the area notoccupied by cell areas. For example, a measure of cell density may bedetermined as a ratio of the total area occupied by cell areas to thetotal area of the tray (or that portion of the tray covered by the cellculture). Alternatively, a measure of cell density may be determined bycomparing the total area occupied by cell areas to a predeterminedvalue.

In another embodiment, a measure of cell density may be determined basedon an observed quantity of cell nuclei for eukaryotic cell cultures. Forexample, an image of a cell culture may be examined to identify eachcell nucleus in the image. A measure of cell density for the eukaryoticcell may be determined based on the observed quantity of cell nuclei.

In another embodiment, a measure of cell density is determined byanalyzing pixels in an image of the cell culture. A first quantity ofedge pixels, and a second quantity of non-edge pixels, are determined. Ameasure of cell density may be determined, for example, by determining aratio of edge pixels to non-edge pixels.

In other embodiments, an image recognition algorithm may be used toidentify features such as patterns of cell growth, different celldensities according to different cell morphologies, etc. A measure ofcell density may be determined based on an analysis of cell growthfeatures.

In another embodiment, a Voronoi algorithm may be used to determine ameasure of cell density.

In another embodiment, a measure of cell density may be determined basedon an overlap measurement.

In another embodiment, a measure of cell density may be determined basedon a measurement of cell movement. For example, a trajectory of one ormore cells may be observed and analyzed. A measure of cell density maybe determined based on the observed movements and trajectories.

In another embodiment, a measure of cell density may be determined basedon RGB measurements.

In another embodiment, a measure of cell density may be determined basedon HSV measurements.

In another embodiment, a measure of cell density may be determined basedon grey scale conversion.

In another embodiment, a measure of cell density may be determined basedon color channel gradients.

In another embodiment, a measure of cell density may be determined basedon index of refraction measurements.

In another embodiment, a measure of cell density may be determined basedon temperature measurements. For example, the temperature of a cellculture may be measured and a measure of cell density may be determinedbased on the temperature measurement.

In another embodiment, a measure of cell density may be determined basedon mass measurements of mass. For example, the mass of a cell culturemay be measured and a measure of cell density may be determined based onthe mass measurement.

In another embodiment, a measure of cell density may be determined basedon weight measurements. For example, the weight of a cell culture may bemeasured and a measure of cell density may be determined based on theweight measurement.

In another embodiment, a measure of cell density may be determined basedon phase measurements. For example, a wave front sensor may be used todetect a wave front.

In another embodiment, a measure of cell density may be determined basedon spectrum measurements.

In another embodiment, a measure of cell density may be determined basedon observations of cell type and/or cell shape.

In another embodiment, a measure of cell density may be determined basedon other methods, such as dielectric spectroscopy, light absorption,light scattering, Fourier transform image analysis, etc.

FIG. 11B is a flowchart of a method of controlling a motion of a cellculture in accordance with another embodiment. At step 1160, an image ofa cell culture is generated. As described herein, camera 260 may obtainan image of a cell culture disposed in tray 220.

At step 1170, a characteristic of the cell culture is determined basedon the image. Image analyzer 280 and/or tray motion controller 290 maydetermine any desired characteristic based on the image data, such ascell density, color, growth patterns, etc.

At step 1180, a motion of the cell culture is adjusted based on thecharacteristic. Because the cell culture is disposed on tray 220, themotion of the cell culture is adjusted by adjusting the movement of tray220. In a manner similar to those described herein, tray motioncontroller 290 may cause tilt mechanism 230 and/or shake mechanism 240to adjust the motion of tray 220, based on the determinedcharacteristic. For example, the motion of tray 220 may be adjusted tooptimize cell growth based on a measured cell density, a measured color,an observed pattern of cell growth, etc. Tray 220 moves in accordancewith the adjusted motion, causing the cell culture to move as well.

In another embodiment, a cell culture may be contained in a containerdisposed on tray 220. FIG. 12A shows a side view of frame 350 and tiltmechanism 230 in accordance with an embodiment. A bag 1220 containing acell culture 1235 is disposed on tray 220. Although not shown, bag 1220can be in fluid communication with the other components of the reactorsystem to optimize cell growth. Tray motion controller 290 may usemethods similar to those described herein to control the motion of tray220 in order to optimize the growth of cell culture 1235. While cellculture 1235 is depicted within bag 1220, cell culture 1235 can bedisposed in any suitable container for cell growth such as a flask. Asfurther shown in FIG. 12B, a plurality of bags 1220 can also be disposedon tray 220. Although not shown, the plurality of bags 1220 can also bein fluid communication with each other in addition to being in fluidcommunication with the other components of the reactor system tooptimize cell growth. FIG. 12C shows a further embodiment in which frame350 is equipped with tilting servos (not labelled) at opposite ends offrame 350 to facilitate access to cell culture 1235.

Various techniques may be used to generate a measure of cell density,determine a characteristic of a cell culture, or to process and/oranalyze an image.

For example, U.S. Pat. No. 9,412,176, issued Aug. 9, 2016, which isincorporated by reference herein in its entirety, discloses methods,systems and articles of manufacture for processing and analyzing images.In particular, U.S. Pat. No. 9,412,176 discloses methods, systems andarticles of manufacture for generating an edge-based feature descriptorfor a digital image. Various embodiments can provide efficientimage-based object recognition capabilities for texture-rich images aswell as texture-poor images. In one embodiment, a plurality of edges aredetected within a digital image. The digital image may be, for example,a video frame of a video stream or a rendered image. The plurality ofedges may be detected based on one of tensor voting and a Canny edgedetection algorithm. An anchor point located along an edge of theplurality of edges is selected. The anchor point may be a featurecorresponding to at least one of a scale-invariant feature transform(SIFT), Fast Retina Keypoint (FREAK), Histograms of Oriented Gradient(HOG), Speeded Up Robust Features (SURF), DAISY, Binary Robust InvariantScalable Keypoints (BRISK), FAST, Binary Robust Independent ElementaryFeatures (BRIEF), Harris Corners, Edges, Gradient Location andOrientation Histogram (GLOH), Energy of image Gradient (EOG) or

Transform Invariant Low-rank Textures (TILT) feature. An analysis gridassociated with the anchor point is generated, the analysis gridincluding a plurality of cells. An analysis grid associated with theanchor point may have a geometric center at the anchor point, and mayinclude one of a polar grid, a radial polar grid or a rectilinear grid.An anchor point normal vector comprising a normal vector of the edge atthe anchor point is calculated. The anchor point normal vector may beone of a Harris matrix eigenvector or a geometric normal vectororthogonal to the edge at a pixel coordinate of the anchor point. One ormore edge pixel normal vectors comprising normal vectors of the edge atone or more locations along the edge within the cells of the analysisgrid are calculated. The edge pixel normal vectors may be one of aHarris matrix eigenvector or a geometric normal vector orthogonal to theedge at a pixel coordinate. A histogram of similarity is generated foreach of one or more cells of the analysis grid, each histogram ofsimilarity being based on a similarity measure between each of the edgepixel normal vectors within a cell and the anchor point normal vector,and a descriptor is generated for the analysis grid based on thehistograms of similarity. Generating the descriptor may includeconcatenating data from the histograms of similarity for one or more ofthe cells of the analysis grid. An image-based object recognition searchmay be facilitated using the descriptor for the analysis grid.

For example, U.S. Pat. No. 9,466,009, issued Oct. 11, 2016, which isincorporated by reference herein in its entirety, discloses apparatus,systems and methods for processing and analyzing images. In particular,U.S. Pat. No. 9,466,009 discloses apparatus, systems and methods forprocessing and analyzing images in which an object data processingsystem can, in real-time, determine which recognition algorithms shouldbe applied to regions of interest in a digital representation. In oneembodiment, a system comprises a plurality of diverse recognitionmodules and a data preprocessing module. Each module represents hardwareconfigured to execute one or more sets of software instructions storedin a non-transitory, computer readable memory. For example, therecognition modules can comprise at least one recognition algorithms(e.g., SIFT, DAISY, ASR, OCR, etc.). Further, the data preprocessingmodule can be configured, via its software instructions, to obtain adigital representation of a scene. The digital representation caninclude one or more modalities of data including image data, video data,sensor data, news data, biometric data, or other types of data. Thepreprocessing module leverages an invariant feature identificationalgorithm, preferably one that operates quickly on the target data, togenerate a set of invariant features from the digital representation.One suitable invariant identification feature algorithm that can beapplied to image data includes the FAST corner detection algorithm. Thepreprocessing module further clusters or otherwise groups the set ofinvariant features into regions of interest where each region ofinterest can have an associated region feature density (e.g., featuresper unit area, feature per unit volume, feature distribution, etc.). Thepreprocessor can then assign each region one or more of the recognitionmodules as a function of the region's feature density. Each recognitionmodule can then be configured to process their respective regions ofinterest according the recognition module's recognition algorithm.

For example, U.S. Pat. No. 9,501,498, issued Nov. 22, 2016, which isincorporated by reference herein in its entirety, discloses apparatus,systems and methods in which real-world objects can be ingested into anobject recognition database using canonical shapes. In one embodiment,an object recognition ingestion system has a canonical shape databaseand an object ingestion engine. The canonical shape database isprogrammed to perform the step of storing one or more shape objectswhere the shape objects represent manageable data objects. Each shapeobject can be considered to represent a known canonical shape or objecttemplate; for example a sphere, cylinder, pyramid, mug, vehicle, orother type of shape. Further the shape objects include geometricalattributes reflecting the aspects of their corresponding shape, aradius, length, width, or other geometrical features for example. Ofparticular note, the shape objects also include one or more referencepoint-of-views (PoVs) that indicate preferred perspectives from which anobject having a corresponding shape could be analyzed. The objectingestion engine can be coupled with the canonical shape database andprogrammed to perform the step of fulfilling the roles orresponsibilities of ingesting object information to populate an objectrecognition database. The engine obtains image data that includes adigital representation of a target object of interest. The enginefurther derives one or more edges of the object from the image data,possibly by executing an implementation of one or more edge detectionalgorithms. Each of the derived edges includes geometrical informationrelating to the nature of the edge (e.g., radius, length, edgels,edgelets, edge descriptors, etc.). The engine can use the informationrelating to the set of edges to obtain a set of shape objects as aresult set from the canonical shape database. In some embodiments, theedge geometrical information is used to identify shape objects that havecompatible or complementary shape attributes as the set of edges. Atleast one of the shape objects in the result set is selected as acandidate shape object for building an object model of the targetobject. Thus, the engine can continue analyzing the target object bygenerating one or more object models of the target object based on theselected shape and the image data. For example, the geometricalattributes of the shape can be adjusted or take on specific valuesrelated to the object, and the image data of the object can be used totexture and/or paint the object model. Further, the engine is programmedto perform the step of using the selected shape's reference PoVs todetermine from which PoVs the object model should be analyzed togenerate key frame information. The engine uses the reference PoVs todrive a set of model key frame PoVs, possibly based on one or more rulesor object symmetry, which will be used for generating the key frames.Further, the engine instantiates a descriptor object model from theobject model where the descriptor model includes recognition algorithmdescriptors (e.g., SIFT, FREAK, FAST, etc.) having locations within oron the object model and relative to the model key frame PoVs. From thedescriptor object model, the engine further compiles one or more keyframe bundles that can be used by other devices to recognize the targetobject. The key frame bundles can include one or more of an image of theobject model from a corresponding key frame PoV, a descriptor related tothe key frame PoV, a normal vector, or other recognition information.The key frame bundles can be stored in an object recognition databasefor consumption by other devices when they are required to recognize thetarget object. Further the key frame bundles can be correlated withobject information, address, content information, applications,software, commands, or there types of media as desired.

For example, U.S. Pat. No. 9,558,426, issued Jan. 31, 2017, which isincorporated by reference herein in its entirety, discloses methods,systems and articles of manufacture for identifying robust featureswithin a training image. Various embodiments can allow for buildingcompact and efficient recognition libraries for image-based objectrecognition. In one embodiment, robust features are identified within atraining image. The training image may be an undistorted image, aninfrared-filtered image, an x-ray image, a 360-degree view image, amachine-view image, a frame of video data, a graphical rendering or aperspective-view of a three-dimensional object, and may be obtained bycapturing a video frame of a video stream via an image capture device.Training features are generated by applying a feature detectionalgorithm to the training image, each training feature having a trainingfeature location within the training image. At least a portion of thetraining image is transformed into a transformed image in accordancewith a predefined image transformation.

A plurality of image transformations may be presented to a user forselection as the predefined image transformation, and the predefinedimage transformation may be selected independently from a method used tocapture the training image. Transform features are generated by applyingthe feature detection algorithm to the transformed image, each transformfeature having a transform feature location within the transformedimage. The training feature locations of the training features aremapped to corresponding training feature transformed locations withinthe transformed image in accordance with the predefined imagetransformation, and a robust feature set is compiled by selecting robustfeatures, wherein each robust feature represents a training featurehaving a training feature transformed location proximal to a transformfeature location of one of the transform features. Each of the trainingfeatures and transform features may be described by a feature descriptorin accordance with the feature detection algorithm. Each of the trainingfeature locations may comprise a pixel coordinate, and each of thetransform feature locations may comprise a transformed pixel coordinate.The feature detection algorithm may include at least one of ascale-invariant feature transform (SIFT), Fast Retina Keypoint (FREAK),Histograms of Oriented Gradient (HOG), Speeded Up Robust Features(SURF), DAISY, Binary Robust Invariant Scalable Keypoints (BRISK), FAST,Binary Robust Independent Elementary Features (BRIEF), Harris Corners,Edges, Gradient Location and Orientation Histogram (GLOH), Energy ofimage Gradient (EOG) or Transform Invariant Low-rank Textures (TILT)feature detection algorithm.

For example, U.S. Pat. No. 9,633,042, issued Apr. 25, 2017, which isincorporated by reference herein in its entirety, discloses apparatuses,systems and methods in which one or more computing devices discoverscene attributes that help enhance feature-based object recognition. Insome embodiments, features are derived from a digital representation ofan image captured by an image sensor and traits are derived from scenetrait sensor data, a particular set of scene trait sensor data beingrelated to a particular digital representation by the time and scene atwhich the data was captured. In some embodiments, an object recognitiontrait identification system includes a trait analysis engine. In someembodiments, the system also includes a scene trait database. In someembodiments, the system also includes an object recognition system andcorresponding object recognition database. The scene trait database isconfigured or programmed to store one or more scene traits thatrepresent the properties of a scene or environment (e.g., lightingconditions, wireless field strengths, gravity, etc.). Each of the scenetraits can have corresponding values (e.g., scalar, vector, etc.) withina scene attribute space. The trait analysis engine leverages the scenetraits in an attempt to differentiate among similar object recognitionfeatures that are commonly associated with an object or with manyobjects. The trait analysis engine is configured to obtain a digitalrepresentation (e.g., images, video, sound, etc.) of an object in ascene and then apply one or more recognition algorithms to the digitalrepresentation to derive one or more features, where the features existwithin a feature space. The engine further compiles a portion of thefeatures into at least one similarity feature set, where the featureswithin the similarity feature set are considered similar to each otheraccording to a similarity measure (e.g., low variance, close proximityin the feature space, clustering, etc.). Although the features withinthe similarity feature set are considered similar to each other withinthe feature space, the engine analyzes the similar features with respectto one or more scene traits in the non-feature, scene attribute spacethereby generating one or more trait variances with respect to knownscene traits. The trait variances provide the engine sufficientinformation to select at least one trait as a distinguishing trait forthe features in the similarity feature set. The features can then bestored in the object recognition database along with the distinguishingtrait information. In alternative embodiments, scene trait analysis isapplied to recognition of all objects across a plurality of scenecaptures, whether or not those objects are associated with descriptorsin a similarity feature set.

For example, U.S. Pat. No. 9,659,033, issued May 23, 2017, which isincorporated by reference herein in its entirety, discloses an apparatuscomprising a memory communicatively coupled to a processor that can beconfigured to operate as an object recognition platform. The memory canstore one or more object-specific metric maps, which map an image colorspace of target object image data to a set of metric values selected toenhance detection of descriptors with respect to a specific object andwith respect to a target algorithm. For example, an object-specificmetric map can map an RGB value from each pixel of a digitalrepresentation of a target object to single metric channel ofrecognition values that can be processed by an image processingalgorithm executing on the processor. The processor, when operating as arecognition engine, can execute various object recognition steps,including for example, obtaining one or more target object-specificmetric maps from the memory, obtaining a digital representation of ascene and including image data (e.g., via a sensor of a device storingthe memory and processor, etc.), generating altered image data using anobject-specific metric map, deriving a descriptor set using an imageanalysis algorithm, and retrieving digital content associated with atarget object as a function of the metric-based descriptor set.

For example, U.S. Pat. No. 9,665,606, issued May 30, 2017, which isincorporated by reference herein in its entirety, discloses apparatus,systems and methods in which one or more computing devices can operateas image processing systems to identify edges representing in image dataand use the identified edges to recognizing objects or classify objectsin a manner that reduces false positives. For example, a method ofenabling a device or a system to take an action based on image data isdisclosed. The method includes obtaining image data having a digitalrepresentation of an object of interest. An image recognition system,which is preferably executed by an image processing device (e.g., atablet, smart phone, kiosk, augmented or virtual reality glasses, etc.)is programmed to perform such method. The method further comprisesanalyzing the image data to generate a collection of edges. For example,the method can include generating a collection of edges by executing animplementation of a co-circularity algorithm on at least a portion ofthe image data related to the object. In more embodiments, edges in thecollection can include a perception measure (e.g., saliency, smoothness,length, etc.) indicating an “edged-nes s” associated with the edge froma perception perspective. From the collection of edges, the imagerecognition system can select a set of candidate edges based in part onthe perception measure. These candidate set of edges represents possiblestarting points from which the image processing device can constructedge-based descriptors. Thus, the method can construct pixel leveledgelets from the image data for the edges in the candidate set. Themethod then derives a plurality of edge-based descriptors from theedgelets where the descriptors represent constellations of edgelets.Once the constellations, or their corresponding descriptors, areidentifying, they can be used to configure a device or the imagerecognition system to take an action based on one or more of thedescriptors in the plurality of edge-based descriptors. For example, theaction can include indexing content related to the object in a contentdatabase (e.g., database, file system, spill tree, k-d tree, etc.)according the associated edge-based descriptors so that the content canbe later retrieved. Another example action includes using the edge-baseddescriptors to query the content database for content related to theobject.

In another embodiment illustrated in FIG. 14, a camera having a fisheyelens may be used to obtain wider or panoramic images. FIG. 14 showscamera 260 supported by arm 364 in accordance with an embodiment. Camera260 includes a fisheye lens 1405. Fisheye lens 1405 enables camera 260to obtain a wide and/or panoramic view of any culture located on tray220.

In another embodiment, a cell control system, such as cell culturesystem 100, including a tray control system similar to tray controlsystem 110 and a computer such as computer 120, are disposed within abioreactor system. FIGS. 15A-15B show components of a bioreactor system1500 in accordance with an embodiment. Bioreactor system 1500 includes acompartment 1520 and a door 1530. Cell culture system 100 is disposedinside compartment 1520. Door 1530 has a closed position, as shown inFIG. 15A, and an open position, as shown in FIG. 15B. Door 1530 may beopened to allow access to cell culture system 1530, for example. Cellculture system 100 may be configured and/or modified to fit and operatewithin compartment 1520.

In various embodiments, the method steps described herein, including themethod steps described in FIGS. 11A and 11B, may be performed in anorder different from the particular order described or shown. In otherembodiments, other steps may be provided, or steps may be eliminated,from the described methods.

Systems, apparatus, and methods described herein may be implementedusing digital circuitry, or using one or more computers using well-knowncomputer processors, memory units, storage devices, computer software,and other components. Typically, a computer includes a processor forexecuting instructions and one or more memories for storing instructionsand data. A computer may also include, or be coupled to, one or moremass storage devices, such as one or more magnetic disks, internal harddisks and removable disks, magneto-optical disks, optical disks, etc.

Systems, apparatus, and methods described herein may be implementedusing computers operating in a client-server relationship. Typically, insuch a system, the client computers are located remotely from the servercomputer and interact via a network. The client-server relationship maybe defined and controlled by computer programs running on the respectiveclient and server computers.

Systems, apparatus, and methods described herein may be used within anetwork-based cloud computing system. In such a network-based cloudcomputing system, a server or another processor that is connected to anetwork communicates with one or more client computers via a network. Aclient computer may communicate with the server via a network browserapplication residing and operating on the client computer, for example.A client computer may store data on the server and access the data viathe network. A client computer may transmit requests for data, orrequests for online services, to the server via the network. The servermay perform requested services and provide data to the clientcomputer(s). The server may also transmit data adapted to cause a clientcomputer to perform a specified function, e.g., to perform acalculation, to display specified data on a screen, etc.

Systems, apparatus, and methods described herein may be implementedusing a computer program product tangibly embodied in an informationcarrier, e.g., in a non-transitory machine-readable storage device, forexecution by a programmable processor; and the method steps describedherein, including one or more of the steps of FIGS. 11A and 11B, may beimplemented using one or more computer programs that are executable bysuch a processor. A computer program is a set of computer programinstructions that can be used, directly or indirectly, in a computer toperform a certain activity or bring about a certain result. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

A high-level block diagram of an exemplary computer that may be used toimplement systems, apparatus and methods described herein is illustratedin FIG. 13. Computer 1300 includes a processor 1301 operatively coupledto a data storage device 1302 and a memory 1303. Processor 1301 controlsthe overall operation of computer 1300 by executing computer programinstructions that define such operations. The computer programinstructions may be stored in data storage device 1302, or othercomputer readable medium, and loaded into memory 1303 when execution ofthe computer program instructions is desired. Thus, the method steps ofFIGS. 11A and 11B can be defined by the computer program instructionsstored in memory 1303 and/or data storage device 1302 and controlled bythe processor 1301 executing the computer program instructions. Forexample, the computer program instructions can be implemented ascomputer executable code programmed by one skilled in the art to performan algorithm defined by the method steps of FIGS. 11A and 11B.Accordingly, by executing the computer program instructions, theprocessor 1301 executes an algorithm defined by the method steps ofFIGS. 11A and 11B. Computer 1300 also includes one or more networkinterfaces 1304 for communicating with other devices via a network.Computer 1300 also includes one or more input/output devices 1305 thatenable user interaction with computer 1300 (e.g., display, keyboard,mouse, speakers, buttons, etc.).

Processor 1301 may include both general and special purposemicroprocessors, and may be the sole processor or one of multipleprocessors of computer 1300. Processor 1301 may include one or morecentral processing units (CPUs), for example. Processor 1301, datastorage device 1302, and/or memory 1303 may include, be supplemented by,or incorporated in, one or more application-specific integrated circuits(ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device 1302 and memory 1303 each include a tangiblenon-transitory computer readable storage medium. Data storage device1302, and memory 1303, may each include high-speed random access memory,such as dynamic random access memory (DRAM), static random access memory(SRAM), double data rate synchronous dynamic random access memory (DDRRAM), or other random access solid state memory devices, and may includenon-volatile memory, such as one or more magnetic disk storage devicessuch as internal hard disks and removable disks, magneto-optical diskstorage devices, optical disk storage devices, flash memory devices,semiconductor memory devices, such as erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), compact disc read-only memory (CD-ROM), digital versatile discread-only memory (DVD-ROM) disks, or other non-volatile solid statestorage devices. Input/output devices 1305 may include peripherals, suchas a printer, scanner, display screen, etc. For example, input/outputdevices 1305 may include a display device such as a cathode ray tube(CRT) or liquid crystal display (LCD) monitor for displaying informationto the user, a keyboard, and a pointing device such as a mouse or atrackball by which the user can provide input to computer 1300.

One skilled in the art will recognize that an implementation of anactual computer or computer system may have other structures and maycontain other components as well, and that FIG. 13 is a high levelrepresentation of some of the components of such a computer forillustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method comprising: providing a cell culture in need of cell growthoptimization, the cell culture being provided in a cell culturecontainer configured to grow cells; generating an image of the cellculture; determining a characteristic of the cell culture to facilitatecell growth optimization based on the image; and adjusting at least onetilting or shaking motion of the cell culture container based on thecharacteristic to facilitate cell growth optimization.
 2. The method ofclaim 1, further comprising: receiving, from a sensor, motion dataindicating a motion of the cell culture container; determining a firstmovement of the cell culture container based on the motion data;adjusting the motion of the cell culture container by determining asecond movement of the cell culture container based on thecharacteristic, the second movement being different from the firstmovement.
 3. The method of claim 1, wherein the characteristic comprisesa measure of cell density.
 4. The method of claim 3, further comprising:using a camera to capture the image of the cell culture; and analyzingthe image data to determine the measure of cell density.
 5. The methodof claim 3, wherein the measure of cell density comprises a secondmeasure of average cell density.
 6. The method of claim 3, whereindetermining the measure of cell density comprises determining a count ofcell clusters.
 7. The method of claim 3, further comprising: determiningwhether the measure of cell density exceeds a predetermined limit; andadjusting a movement of the cell culture in response to determining thatthe measure of cell density exceeds the predetermined limit.
 8. Themethod of claim 3, wherein determining the measure of cell densitycomprises: identifying one or more cell morphologies among cells in thecell culture; and determining one or more second measures of celldensities based on the one or more cell morphologies.
 9. The method ofclaim 1, wherein the cell culture container is disposed on a trayconfigured to receive the cell culture container and configured to tiltor shake the cell culture container.
 10. The method of claim 9, wherein:adjusting the tilting motion of the cell culture container includescausing the cell culture container on the tray to tilt back and forth atone of a lower rate and a higher rate; adjusting the shaking motion ofthe cell culture container on the tray to shake back and forth at one ofa lower rate and a higher rate; or adjusting both the tilting motion andshaking motion of the cell culture container.
 11. An apparatuscomprising: a first device configured to: hold a cell culture containercontaining a cell culture in need of cell growth optimization, the cellculture container being configured to grow cells; and cause a movementof the cell culture container; a second device configured to generate animage of the cell culture; and at least one processor configured to:determine a characteristic of the cell culture to facilitate cell growthoptimization based on the image; and cause the first device to adjust atleast one tilting or shaking motion of the cell culture container basedon the characteristic to facilitate cell growth optimization.
 12. Theapparatus of claim 11, wherein the characteristic comprises a measure ofcell density.
 13. The apparatus of claim 12, wherein the processor isfurther configured to: determine a measure of average cell density basedon the image.
 14. The apparatus of claim 11, wherein the processor isfurther configured to: determine a count of cell clusters based on theimage; determine the measure of cell density based on the count of cellclusters; or determine both the count of cell clusters and the measureof cell density.
 15. A system comprising: a tray configured to hold acell culture container containing a cell culture in need of cell growthoptimization, the cell culture container being configured to grow cells;a camera configured to capture an image of the cell culture; a deviceconfigured to control a movement of the tray; and a processor configuredto: determine a first movement of the tray; receive from the camera datarepresenting an image of the cell culture; determine a characteristic ofthe cell culture to facilitate cell growth optimization based on theimage data; determine a second movement of the tray based on thecharacteristic, the second movement being different from the firstmovement; and cause the device to cause the tray to move in accordancewith the second movement to facilitate cell growth optimization.
 16. Thesystem of claim 15, further comprising: a sensor configured to obtainmotion data indicating a motion of the tray, wherein the processor isfurther configured to: receive the motion data from the sensor; anddetermine the first movement of the tray based on the motion data. 17.The system of claim 15, wherein the characteristic comprises a measureof cell density.
 18. The system of claim 17, wherein the processor isfurther configured to: determine a second measure of average celldensity based on the image data.
 19. The system of claim 18, wherein theprocessor is further configured to: determine a count of cell clusters;determine the measure of cell density based on the count of cellclusters; or determine both the count of cell clusters and the measureof cell density.
 20. The system of claim 17, wherein the processor isfurther configured to: determine that the measure of cell densityexceeds a predetermined limit; determine the second movement in responseto the determination that that the measure of cell density exceeds thepredetermined limit; or determine both the measure of cell densityexceeds a predetermined limit and the second movement.
 21. The system ofclaim 15, wherein the processor is further configured to: adjust atleast one tilting or shaking motion of the tray to determine the secondmovement of the tray.
 22. The system of claim 21, wherein the processoris further configured to perform one of the following: cause the tray totilt back and forth at one of a lower rate and a higher rate; cause thetray to shake back and forth at one of a lower rate and a higher rate;or cause the tray to both tilt back and forth and shake back and forth.23. The method of claim 1, wherein the characteristic determined fromthe image comprises cell morphology.
 24. The method of claim 1, whereinthe characteristic determined from the image comprises cell growthpatterns.
 25. The apparatus of claim 11, wherein the characteristicdetermined from the image comprises cell morphology.
 26. The apparatusof claim 11, wherein the characteristic determined from the imagecomprises cell growth patterns.
 27. The system of claim 15, wherein thecharacteristic determined from the image comprises cell morphology. 28.The system of claim 15, wherein the characteristic determined from theimage comprises growth patterns.